Robust Collaborative Learning of Patch-level and Image-level Annotations for Diabetic Retinopathy Grading from Fundus Image

• URL: http://arxiv.org/abs/2008.00610v2
• Date: Thu, 18 Mar 2021 07:35:45 GMT
• Title: Robust Collaborative Learning of Patch-level and Image-level Annotations for Diabetic Retinopathy Grading from Fundus Image
• Authors: Yehui Yang, Fangxin Shang, Binghong Wu, Dalu Yang, Lei Wang, Yanwu Xu, Wensheng Zhang, Tianzhu Zhang
• Abstract summary: We present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bi-directionally exchange the fine-grained lesion and image-level grade information. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience.
• Score: 33.904136933213735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network (CNN) based algorithms treat DR grading as a classification task via image-level annotations. However, these algorithms have not fully explored the valuable information in the DR-related lesions. In this paper, we present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bi-directionally exchange the fine-grained lesion and image-level grade information. As a result, it exploits more discriminative features for DR grading. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience. By testing on datasets of different distributions (such as label and camera), we prove that our algorithm is robust when facing image quality and distribution variations that commonly exist in real-world practice. We inspect the proposed framework through extensive ablation studies to indicate the effectiveness and necessity of each motivation. The code and some valuable annotations are now publicly available.

Related papers

• RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy [83.4885991036141]
  RIDE is a learning-based method for rotation-equivariant detection and invariant description. It is trained in a self-supervised manner on a large curation of endoscopic images. It sets a new state-of-the-art performance on matching and relative pose estimation tasks.
  arXiv  Detail & Related papers  (2023-09-18T08:16:30Z)
• Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods [0.0]
  Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
  arXiv  Detail & Related papers  (2023-04-29T16:11:34Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Self-Supervised Endoscopic Image Key-Points Matching [1.3764085113103222]
  This paper proposes a novel self-supervised approach for endoscopic image matching based on deep learning techniques. Our method outperformed standard hand-crafted local feature descriptors in terms of precision and recall.
  arXiv  Detail & Related papers  (2022-08-24T10:47:21Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images [2.498907460918493]
  We propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy grading. Our proposed framework performs outstandingly on DR grading in terms of both linear evaluation and transfer capacity evaluation.
  arXiv  Detail & Related papers  (2021-07-17T16:30:30Z)
• Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification [75.27973258196934]
  We propose a novel Categorical Relation-preserving Contrastive Knowledge Distillation (CRCKD) algorithm, which takes the commonly used mean-teacher model as the supervisor. With this regularization, the feature distribution of the student model shows higher intra-class similarity and inter-class variance. With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively.
  arXiv  Detail & Related papers  (2021-07-07T13:56:38Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Learning Discriminative Representations for Fine-Grained Diabetic Retinopathy Grading [6.129288755571804]
  Diabetic retinopathy is one of the leading causes of blindness. To determine the disease severity levels, ophthalmologists need to focus on the discriminative parts of the fundus images.
  arXiv  Detail & Related papers  (2020-11-04T04:16:55Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)
• ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis [0.46137254657294535]
  We describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI. This algorithm, called ExplAIn, learns to segment and categorize lesions in images. We expect this new framework, which jointly offers high classification performance and explainability, to facilitate AI deployment.
  arXiv  Detail & Related papers  (2020-08-13T07:34:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.